Identification of parameters in chemical kinetics using a hybrid algorithm of artificial bee colony algorithm and simplex

Authors:
Guangzhou Chen;Jiaquan Wang;Ruzhong Li
Affiliations:
Department of Environmental Engineering, Anhui University of Architecture, Hefei, China;School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, China;School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

Citing 4
Cited 1

Empirical Study of Hybrid Particle Swarm Optimizers with the Simplex Method Operator

ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
On the performance of artificial bee colony (ABC) algorithm

Applied Soft Computing
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

Journal of Global Optimization
Hierarchical Differential Evolution for Parameter Estimation in Chemical Kinetics

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence

A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization

Computational Optimization and Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

Parameter identification is a key step in establishing kinetic models. Aimed at the above problem, it can be transformed into an optimization problem by constructing objective function that minimizes simulation errors. Two examples of chemical kinetics are analyzed. Among them, the second case is a more complicated nonlinear problem, so using ABC individually can not obtain better results. Then a hybrid algorithm of ABC and simplex is proposed. The method firstly uses ABC to carry out global search so as to obtain better initial point. Secondly, simplex is employed to process local search based on the above initial point. Compared with that of modified genetic algorithm, hierarchical differential evolution, adaptive differential evolution, the results show that the new hybrid algorithm can obtain better optimization precision by combining global searching ability of ABC with strong local searching ability of simplex. So it is an effective optimization method.